Holt Model Research Articles

التنبُّؤ بسعر الصرف الفوري لليورو دولار في سوق (FOREX) استعمال المفاضلة بين طريقة هولت والشبكات العصبية الاصطناعية

In this paper , the time series data of the daily exchange rate of the euro against the dollar were used for the period from (12/7/2020 H:1 AM-10/12/2020 H:7 AM) with a total of (79) Observations as (120) Observations for comparison with the forecast values obtained using the two methods, to make the comparison process for future forecasts for the period from (10/12/2020 H:8 AM- 15/12/2020 H:7 AM) with a total of (120) Observations. To predict them, between the double exponential smoothing model (Holt method) and the feed-forward artificial neural network model using the two algorithms (Incremental Back Propagation algorithm, Quick Propagation algorithm).These methods are characterized by high accuracy and flexibility of these methods in the process of analyzing the time series, the results of the application showed that the most efficient and optimal model for representing the time series data is the artificial neural network model [2-10-1] using the Quick Propagation algorithm for the daily exchange rate of the euro against the dollar according to the criterion The mean square error ( MSE), has given lower indicators than the indicators of the artificial neural network model [2-10-1] using the Incremental Back Propagation algorithm, and the double exponential smoothing model (Holt method) when using (α =0.9 and = 0.1), which clearly indicates that it is the appropriate and efficient model for estimating future forecasts for the period from (10/12/2020 H:8 AM- 15/12/2020 H:7 AM). Where these values showed consistency with their counterparts in the original series, and provided us with a future picture of the reality of the daily exchange rate of the euro against the dollar for that period. Therefore, the artificial neural network model provided better future predictions than those provided by the double exponential smoothing model (Holt method), according to the standard of mean square error ( MSE), it gave less indicators than the indicators of the Holt model. The ready-made statistical programs MinitabV18 were used in the statistical side, and the ready-made neural network system program known as Alyuda NeuroIntelligence was used in the neural networks side.

Model‐based offline reinforcement learning for sustainable fishery management

AbstractFisheries, as indispensable natural resources for human, need to be managed with both short‐term economical benefits and long‐term sustainability in consideration. This has remained a challenge, because the population and catch dynamics of the fisheries are complex and noisy, while the data available is often scarce and only provides partial information on the dynamics. To address these challenges, we formulate the population and catch dynamics as a Partially Observable Markov Decision Process (POMDP), and propose a model‐based offline reinforcement learning approach to learn an optimal management policy. Our approach allows learning fishery management policies from possibly incomplete fishery data generated by a stochastic fishery system. This involves first learning a POMDP fishery model using a novel least squares approach, and then computing the optimal policy for the learned POMDP. The learned fishery dynamics model is useful for explaining the resulting policy's performance. We perform systematic and comprehensive simulation study to quantify the effects of stochasticity in fishery dynamics, proliferation rates, missing values in fishery data, dynamics model misspecification, and variability of effort (e.g., the number of boat days). When the effort is sufficiently variable and the noise is moderate, our method can produce a competitive policy that achieves 85% of the optimal value, even for the hardest case of noisy incomplete data and a misspecified model. Interestingly, the learned policies seem to be robust in the presence of model learning errors. However, non‐identifiability kicks in if there is insufficient variability in the effort level and the fishery system is stochastic. This often results in poor policies, highlighting the need for sufficiently informative data. We also provide a theoretical analysis on model misspecification and discuss the tendency of a Schaefer model to overfit compared with a Beverton–Holt model.

Predicting Post-Production Biomass Prices

This paper presents the application of prediction in the analysis of market price volatility in Polish conditions of wood processing by-products in the form of biomass. The ARIMA model, which takes into account cyclical, seasonal, irregular fluctuations of historical data on the basis of which the forecast and long-term trends of selected wood products were made, was used in predicting prices. Comparisons were made between the ARIMA prediction method and the multiplicative Winters–Holt model. During the period studied (2017–2022), the changes in the market price of biomass were characterized by a wide spread of values. On average, the price of these products increased from 2017 to the end of 2022 by 125%. The price prediction analysis showed seasonal fluctuations in the case of wood chips. The uncertainty in price prediction is due to changes in supply resulting from the influence of global factors. The Diebold–Mariano test of matching accuracy confirms that the price prediction of the analyzed by-product sorts using the ARIMA and WH models is possible. The conclusion reached by comparing these two methods is that each can be used under certain market conditions of certain assortments. In the case of a stable wood product, the choice of the ARIMA model should be resolved, while in the case of price volatile products, WH will be a better choice. The difference between the predicted and actual price with ARIMA ranged from 2.4% to 11.6% and for WH from 3.7% to 29.8%.

Balanced harvesting of dynamical discrete Ricker & Beverton–Holt system

In this paper, we discuss a dynamic version of the Ricker & Beverton–Holt (R&B–H)model, which states a two populations commercial fishery, where stock-recruitment effects cannot be neglected. We investigate a stable and balanced fish catches strategy that local breeding stocks are preserved abundant and stable. The Ricker & Beverton–Holt model in commercial fishery, based on the Allee effect, is combined by Beverton–Holt Logistic equation and Ricker model, yn+1=azn1+pzne−yn,zn+1=byn1+qyne−zn. where the parameters a,b,p,q, are positive real numbers and the initial values y0,z0 are arbitrary nonnegative real numbers. Using the mean value theorem and Lyapunov functional skills, we obtain the sufficient conditions to guarantee the bounded and persistent solution of the Ricker & Beverton–Holt model, and global asymptotic stability of the equilibrium. Moreover, three numerical examples are given to elaborate on the results.

Statistical modelling and forecasting annual sugarcane production in India: Using various time series models

AbstractThis paper proposes a comparison of various time series forecasting models to forecast annual data on sugarcane production over 63 years from 1960 to 2022. In this research, the Mean Forecast Model, the Naive Model, the Simple Exponential Smoothing Model, Holt's model, and the Autoregressive Integrated Moving Average time series models have all been used to make effective and accurate predictions for sugarcane. Different scale‐dependent error forecasting techniques and residual analysis have been used to examine the forecasting accuracy of these time series models. SE of Residuals, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Akaike's Information Criterion (AIC) are used to analyse the forecast's accuracy. The best model has been selected based on the predictions with the lowest value, according to the three‐performance metrics of RMSE, MAE, and AIC. The estimated sugarcane production shows an increasing trend for the next 10 years and is projected to be 37,763.38 million tonnes in the year 2032. Further, empirical results support the plan and execution of viable strategies to advance sugarcane production in India to fulfil the utilisation need of the increasing population and further improve food security.

Modeling Dynamic Compaction of Porous Materials

To model dynamic compaction of a porous material we need: 1) an equation of state (EOS) for the porous material in terms of the EOS of its matrix; and 2) a compaction law. For an EOS people usually use Hermann's suggestion, as in his Pα [1] model. For a compaction law people usually use the results of a spherical shell collapse analysis (Carroll and Holt model [2]). In their original paper Carroll and Holt do both: the quasi-static shell collapse and the dynamic shell collapse. In their dynamic analysis, however, they ignore density changes of the matrix. In what follows we: 1) revisit the spherical shell collapse problem but with density changes taken into account; 2) develop a dynamic compaction law based on our overstress principle; 3) implement the different compaction laws mentioned above in a hydro-code; and 4) run a planar impact problem and compare histories and profiles obtained with the different compaction laws. We find that: 1) dynamic compaction laws give entirely different results from quasi-static compaction laws; 2) taking density changes into account do make a certain difference.

Machine Learning and Deep Learning Based Time Series Prediction and Forecasting of Ten Nations' COVID-19 Pandemic.

In the paper, the authors investigated and predicted the future environmental circumstances of a COVID-19 to minimize its effects using artificial intelligence techniques. The experimental investigation of COVID-19 instances has been performed in ten countries, including India, the United States, Russia, Argentina, Brazil, Colombia, Italy, Turkey, Germany, and France using machine learning, deep learning, and time series models. The confirmed, deceased, and recovered datasets from January 22, 2020, to May 29, 2021, of Novel COVID-19 cases were considered from the Kaggle COVID dataset repository. The country-wise Exploratory Data Analysis visually represents the active, recovered, closed, and death cases from March 2020 to May 2021. The data are pre-processed and scaled using a MinMax scaler to extract and normalize the features to obtain an accurate prediction rate. The proposed methodology employs Random Forest Regressor, Decision Tree Regressor, K Nearest Regressor, Lasso Regression, Linear Regression, Bayesian Regression, Theilsen Regression, Kernel Ridge Regressor, RANSAC Regressor, XG Boost, Elastic Net Regressor, Facebook Prophet Model, Holt Model, Stacked Long Short-Term Memory, and Stacked Gated Recurrent Units to predict active COVID-19 confirmed, death, and recovered cases. Out of different machine learning, deep learning, and time series models, Random Forest Regressor, Facebook Prophet, and Stacked LSTM outperformed to predict the best results for COVID-19 instances with the lowest root-mean-square and highest R 2 score values.

Application of Machine Learning in Forecasting the Impact of Mining Deformation: A Case Study of Underground Copper Mines in Poland

Open access to SAR data from the Sentinel 1 missions allows analyses of long-term ground surface changes. The current data-acquisition frequency of 12 days facilitates the continuous monitoring of phenomena such as volcanic and tectonic activity or mining-related deformations. SAR data are increasingly also used as input data in forecasting phenomena on the basis of machine learning. This article presents the possibility of using selected machine learning algorithms in forecasting the influence of underground mining activity on the ground surface. The study was performed for a mining protective area with a surface of over 500 km2 and located in western Poland. The ground surface displacements were calculated for the period from November 2014 to July 2021, with the use of the Small Baseline Subset (SBAS) method. The forecasts were performed for a total of 22 identified subsidence troughs. Each of the troughs was provided with two profiles, with a total of more than 10,000 identified points. The selected algorithms served to prepare 180-day displacement forecasts. The best results (significantly better than the baseline) were obtained with the ARIMA and Holt models. Linear models also provided better results than the baseline and their performance was very good at up to 2 months forecasting. Tree-based models including their sophisticated ensemble versions: bagging (Random Forest, Extra Trees) and boosting (XGBoost, LightGBM, CatBoost, Gradient Boosting, Hist Gradient Boosting) cannot be used for this type of predictions since Decision Trees are not able to extrapolate and thus are not a valid stand-alone tool for forecasting in this type of problems. A combination of satellite remote sensing data and machine learning facilitated both the simultaneous quasi-permanent monitoring of ground surface displacements and their forecasting in a relatively long time period.

Multiple crop yield estimation and forecasting using MERRA-2 model, satellite-gauge and MODIS satellite data by time series and regression modelling approach

Multiple crop yield estimation and forecasting have become essential because climate change and land degradation are deteriorating production of crops. The present study presents multiple crop yield estimation and forecasting efficiently and precisely using MERRA-2 model, satellite-gauge and MODIS-Terra satellite data by regression and time series models. Rice, wheat, maize, rapeseed & mustard, and millet crops yield data of India was used for crop yield estimation and forecasting using Multiple Linear Regression (MLR), Decomposition, Holt, Winters and Autoregressive Integrated Moving Average (ARIMA) models. Models were developed using crops yield historical data, weather variables (temperature, precipitation, surface radiation) and pollutants (O3, CO) data of 1982–83 to 2014–15. Overall best correlation (R = 0.96) and poor correlation (R = 0.56) was found between observed and estimated rice and rapeseed & mustard crops using Decomposition and MLR models, respectively. After rigorous skill assessment and out of more than 20 combinations of ARIMA models, the ARIMA (0, 1, 1) time series model performed the best with Root Mean Square Error (RMSE = 4.59%) for wheat crop estimation. Forecasted crop yields were successfully cross-validated using four years’ data (2015–16, 2016–17, 2017–18 and 2018–19) for regression and time series models by comparing Relative Error (RE%) between observed and forecasted crops yields. Results were statistically analyzed using correlation coefficient (R), Bayesian Information Criteria (BIC), RMSE%, Mean Absolute Percentage Error (MAPE%), t-value and p-value. The crop yield estimation and forecasting results showed reasonably quantifiable information with the actual datasets. The outcomes of the present study have policy level implications for compensation to farming communities for the loss of crops.

Square root identities for harvested Beverton–Holt models

We introduce the term net-proliferation rate for a class of harvested single species models, where harvest is assumed to reduce the survival probability of individuals. Following the classical maximum sustainable yield calculations, we establish relations between the proliferation and net-proliferation that are economically and sustainably favored. The resulting square root identities are analytically derived for species following the Beverton–Holt recurrence considering three levels of complexity. To discuss the generalization of the results, we compare the square root result to the optimal survival rate of the Pella–Tomlinson model. Furthermore, to test the practical relevance of the square root identities, we fit a stochastic Pella–Tomlinson model to observed Barramundi fishery data from the Southern Gulf of Carpentaria, Australia. The results show that for the estimated model parameters, the equilibrium biomass levels resulting from the MSY harvest and the square root harvest are similar, supporting the claim that the square root harvest can serve as a rule-of-thumb. This application, with its inherited model uncertainty, sparks a risk sensitivity analysis regarding the probability of populations falling below an unsustainable threshold. Characterization of such sensitivity helps in the understanding of both dangers of overfishing and potential remedies.

Study on Support Mechanisms for Renewable Energy Sources in Poland

Poland, as a member of the European Union (EU), has to fulfill the obligations resulting from its membership in it. It is necessary to comply with numerous directives and other legal provisions adopted by the European Commission in the field of the energy market and production. Meeting the demands of the European Green Deal, as well as the solutions presented in the Fit for 55 package, is very difficult. In Poland, coal is still used in over 67% of electricity production. This article presents an attempt to join the multi-threaded discussion on renewable energy sources (RES) and the possibility of increasing their share in Poland’s energy mix. This article defines the RES support mechanisms in Poland, presents the support systems and instruments functioning within them, and also provides a statistical prediction of trends in energy production from RES for upcoming years. Research utilized the Brown, Holt, and Winters models and the cause-and-effect model. The research conducted in this article shows that Poland must make significant efforts to decarbonize the economy; in addition, the too quickly changing RES support system is not conducive to the development of these sources.

Global Attractivity for Nonautonomous Delay-Differential Equations with Mixed Monotonicity and Two Delays

AbstractIn this paper we study the scalar delay differential equation $$\begin{aligned} x'(t)=\alpha (t) x(t-g_{1}(t)) f(a(t),x(t-g_{2}(t)))-\beta (t) x(t) \end{aligned}$$ x ′ ( t ) = α ( t ) x ( t - g 1 ( t ) ) f ( a ( t ) , x ( t - g 2 ( t ) ) ) - β ( t ) x ( t ) where f is decreasing in both arguments and the coefficients are positive and bounded. Sufficient conditions for the permanence and global attractivity for a fixed positive solution are derived. We apply our results to nonautonomous variants of Nicholson’s blowfly equation and the Beverton–Holt model.

A Conditioned Forecasting Model: A-priori Screening Validation Testing

Context The forecasting literature over the last three decades documents that judgmental conditions on the performance of the forecasting model are often used to rationalize the acceptance of the intel from a forecasting model that will be used in creating an action-plan. However, rarely are these judgmental-conditioning protocols recorded as they should be to intelligently process the interactions of the conditioning protocols with possible adjustments made in the forecasts. 
 
 Focus In this research report, we will offer, four judgmental conditioning aspects that are not infrequently used by managers of forecasting divisions. Specifically, the acceptance contingencies of a forecasting model under evaluation scrutiny are: (i) The desired magnitude of the Median benchmarked Precision is in evidence, (ii) The Holdback is in the (1-FPE) Confidence Interval, (iii) The Pearson Product Moment Correlation-Null of the residuals is not rejected, and (iv) The Autocorrelation-Null of the residuals is not rejected. Each of these four conditioning aspects will be evaluated for two standard models typically in the panoply of forecasters: The Two-Parameter [Intercept & Slope] Linear OLS-Regression & the ARIMA(0, 2, 2)/Holt models. The measure of interest for ALL of the selected inferential analyses is: How often do selections among these conditioning aspects result in the forecasting model being rejected as informing the decision-making process? Results Surprisingly, the range of Failures for the conditions tested ranged grosso modo in the interval:{40% to 80%}depending on the nature of the Conditions. These implications are discussed.

Economics of Oil Seed Exports in India: An Application of Holt-Forecasts

This article intended to examine the export performance of oil seed crops in India from 1987 to 2019 using the Compound Annual Growth Rate (CAGR), Cuddy-Della Valle instability index, as well as forecast export quantity and value to 2025. The analysis focuses mainly on two major oil seed crops: groundnut and soyabean. The research of oil seed crop export growth trends over time revealed a positive growth rate for both export quantity and export unit value. The potential for Indian oil seed crop in the world market during the post-liberalization period were shown by the strong growth rates of soyabean goods combined with high export instability indices. When compared to soyabean, the annual growth rate of groundnut was the lowest, with instability showing a wide range across the entire period. Holt models were created in this paper to estimate oil seed export quantity and prices, with the top models chosen by comparing Akaike Information Criteria (AIC), Mean Absolute Percent Error (MAPE). Further, groundnut export quantity and price forecasts showed positive development throughout the forecast period, whereas, soyabean export price forecasts showed a 0.01 percent increase from 2021 to 2025. To improve the worldwide performance of the Indian oil seed industry, the government should make appropriate amendments to oil seed export policies and domestic supportive measures, as well as adopt appropriate value adds and market diversification strategies.

A multistage Gompertz life‐cycle model applied to threatened Chinook salmon

AbstractBecause the Gompertz model has a long history of use as a population model, we analyzed its properties as a multistage stock‐recruitment model. We found that if a life‐cycle model is a sequence of linked Gompertz stock‐recruitment models at each life stage, then it is also a Gompertz stock‐recruitment model. This is similar to the well‐known result for the Beverton–Holt stock‐recruitment model. The Gompertz model is guaranteed to yield least squares estimates and therefore, can offer a distinct advantage over the Beverton–Holt model, which cannot be fit using linear regression and may not yield valid maximum likelihood estimates. We illustrate the use of this multistage modelling framework by applying it to Snake River spring/summer Chinook salmon (Oncorhynchus tshawytscha). We found that the Gompertz model fit the data better than the Beverton–Holt Model, and yielded similar carrying capacity estimates for both juveniles and adults. Past work by others on these populations has usually assumed that parr‐to‐adult survival is density independent. However, we found that this assumption may be incorrect: when we applied two‐stage models to these populations, we found density dependence at the juvenile‐to‐adult stage. This suggests that life‐cycle modelling to date has been overly optimistic about the benefits of survival rate increases in the hydro‐system and elsewhere to improve the viability of salmon populations threatened with high extinction risk.

DDoS attack detection and defence mechanism based on second-order exponential smoothing: Holt's model

DDoS attack detection and defence mechanism based on second-order exponential smoothing: Holt's model

DDoS attack detection and defence mechanism based on second-order exponential smoothing: Holt's model

Technological progress and digitisation are primarily assisted by the growth of internet. The web has now become a national asset, and all national security relies on it as well. But these emerging developments have also brought with them unparalleled network threats. Among them, a strong and more powerful attack on the internet is a distributed denial of service attack. In this work we propose a novel framework for DDoS detection. To detect anomalous variations in mean distance values the technique of second order exponential smoothing/Holt's method is used. In the proposed context, the DDoS defence module is based on the principle of the rate limitation of incoming traffic on the basis of bandwidth and demand rates from a device connected. The experimental verification of proposed method is done using the NS2 simulator and the results are evaluated with detection rate, throughput and false positive rate.

Modelos para previsão das taxas de incidência e mortalidade do câncer do colo do útero

Objective: analyze and predict cervical cancer incidence and mortality rates, using time series models. Method: ecological study, carried out in the capital Matogrossense. The incidence data were from the Population Based Cancer Registry, from 2000 to 2012, and mortality data, from 1981 to 2018, obtained from the Mortality Information System. And analyzed by the ARIMA and HOLT models. Results: the series of the incidence rate of carcinoma in situ, the appropriate model was the AR (1). Estimated projections (2013-2014) fluctuated between 15,54 and 16,45 per hundred a thousand women. In the invasive cervixl series, the model was Holt’s, and the predictions (2013-2014), with values between 11,28 to 8,9 per 100 thousand women. In the mortality rate, the satisfactory model was ARMA (1,1), whose predictions (2019-2020) fluctuated between 9,46 and 9,62 per 100 thousand women. Conclusion: in the three cases, the predicted rates presented levels close to those observed, indicating that the models allowed estimating and predicting, in an adequate way. It was also possible to observe a slight increase in in situ cancer and a decline in the incidence of invasive cancer, while mortality showed stability in the results and predictions. Descriptors: ARIMA; Cervical Cancer; Incidence; Mortality; Holt's method.

ОVERVIEW OF STATISTICAL METHODS FOR FORECAST DEVELOPMENT

Having a general idea of the nature of the forecast and understanding the general methodology of forecasting in general, it is advisable for analytical departments, economic departments, management units of enterprises, banks, firms (of any socio-economic objects) to develop at least short-term forecasts based on indicators of their activity in modern conditions coopetitions to understand trends in changes in these indicators. As a result of the conducted research, the basic principles that must be observed when developing forecasts are revealed, as well as a detailed description and features of statistical auto-projective forecasting methods are given: random walk models containing a free term or it; models characterizing a deterministic trend with random fluctuations around the trend; moving average models; exponential smoothing models using simple exponential smoothing, linear, quadratic and seasonal smoothing (Brown, Holt, and Winters models); integrated presentation of autoregressive models and moving average models (parametric ARIMA models). In addition, the conducted research confirmed that the significance of the developed forecast and the level of confidence in the obtained future values of the indicators depends on the quality of the developed models. The criteria for checking the quality of the developed forecasts are: the Akaike information criterion, which evaluates the quality of the model compared to each other; the Hannan-Quinn Criterion information criterion, which is used to compare models with a different number of parameters and is an alternative to the Akaike information criterion; the Schwarz-Bayesian information Criterion, which compares the quality of a model relative to each other using a likelihood function; the mean squared error value, the absolute value of the mean squared error and the absolute value of the mean squared error expressed as a percentage.

The Madingley general ecosystem model predicts bushmeat yields, species extinction rates and ecosystem‐level impacts of bushmeat harvesting

Traditional approaches to guiding decisions about harvesting bushmeat often employ single‐species population dynamic models, which require species‐ and location‐specific data, are missing ecological processes such as multi‐trophic interactions, cannot represent multi‐species harvesting and cannot predict the broader ecosystem impacts of harvesting. In order to explore an alternative approach to devising sustainable harvesting strategies, we employ the Madingley general ecosystem model, which can simulate ecosystem dynamics in response to multi‐species harvesting given nothing other than location‐specific climate data. We used the model to examine yield, extinctions and broader ecosystem impacts, for a range of harvesting intensities of duiker‐sized endothermic herbivores. Duiker antelope (such asCephalophus callipygusandCephalophus dorsalis) are the most heavily hunted species in sub‐Saharan Africa, contributing 34–95% of all bushmeat in the Congo Basin. Across a range of harvesting rates, the Madingley model gave estimates for optimal harvesting rate, and extinction rate, that were qualitatively and quantitatively similar to the estimates from conventional single‐species Beverton–Holt model. Predicted yields were somewhat greater (around five times, on average) for the Madingley model than the Beverton–Holt, which is partly attributable to the fact that the Madingley simulates multi‐species harvesting from an initially pristine ecosystem. Also, the Madingley model predicted a background local extinction probability for the target species of at least 10%. At medium and high levels of harvesting of duiker‐sized herbivores, the Madingley model predicted statistically significant, but moderate, reductions in the densities of the targeted functional group; increases in small‐bodied herbivores; decreases in large‐bodied carnivores; and minimal ecosystem‐level impacts overall. The results illustrate how general ecosystem models such as the Madingley model could potentially be used more widely to help estimate sustainable harvesting rates, bushmeat yields and broader ecosystem impacts across different locations and target species.